If the hypothesis is that "everyone is tightening spend on usage", then anthropic is boned, if that holds true. Look at the price to performance for any benchmark and compare OAI and Anthropic, and this would be a pretty clearly terrible reality for Anthropic. OpenAI absolutely blows Anthropic out of the water when it comes to cost, if you pay per token.
I know this is tech sector funny money and we do just disregard profit and judge companies on revenue alone, but the 5.5 models that just released are quite a bit cheaper than the 5.0 tier.
I wouldn't really be surprised if better model efficiency was part of the reason for a revenue stall, but I would also expect their costs to have dropped a fair bit too (although I have no idea whether that corresponds to an actual cost reduction or if it's all tied up in hardware and land that they already bought and will need to hit more utilisation of now.)
Opus 5.5 came out right at the end of the quarter, September 22. I'm confident that it caused more usage growth since then. I'm sure there are a lot of people rooting for their IPO to fail but they are still clearly in the lead on coding and will continue to do well until someone definitively passes them.
> they are still clearly in the lead on coding and will continue to do well until someone definitively passes them.
I think the first implying the second might be a bit of a stretch. At some point, companies are going to start looking at their AI spending and start negotiating deals. OpenAI is right behind them and Anthropic is around 8x more expensive (Sol 6.1 vs Opus 5.5 based on Artificial Intelligence's cost per task; cost per token is a somewhat useless metric when different models use vastly different numbers of tokens for the same thing). Chinese models like Deepseek 4.1 Flash are cheap, can be self-hosted, and while certainly behind Claude Opus 5.5, are still good.
Lots of companies buy "inferior" products like Microsoft Teams. Over the next 5 years, it seems likely that many models will get really good to the point that people often won't be able to tell any difference. Even if something isn't "definitively" better, companies might say "our engineers can't tell the difference and we're cutting our costs by 85%."
It's not absurd to think that a company would use its own harness or an open source harness and/or put a proxy in to route requests variably. For two months, they have engineers using different models unbeknownst to them and they measure things like how often the engineers have to correct or redirect the model. At the end of two months, they might find little difference in performance and switch to a much lower cost model.
It's not like it'd be crazy hard to analyze workers frustration as they did their jobs and figure out if one model was actually better. They could log all the conversations and it's not like AI can't easily determine things like "no, that's completely wrong" as a phrase showing that the agent was really off base.
I think a big part of the next 5-10 years is going to be about cost/efficiency.
I’m not sure it’s a binary thing that contradicts.
I want AI and these companies to succeed. And I especially want new companies to succeed. But I want them to be valued far more realistically. Because if they have to be worth trillions, then there’s a real motivation to do what it takes to realize that valuation. And that’s how you birth a Zuckerberg.
I’m not rooting for them to fail. I’m rooting for the correction to happen sooner than later.
> But I want them to be valued far more realistically.
They are. That's the problem. The obligations that these companies are, on paper, supposed to meet require these valuations.
If you value Anthropic at $500b that's still a fuckload and vastly less than their obligations and a lot of people are going to say "well wait a minute how am I going to get paid".
The amount of spend going on, and planned, is insane and underpins a lot of this, and is why people are skeptical of the valuations. It has nothing to do with "Wanting" AI to fail and just a reasonable bet based on the math. For them to pay back what they have on their balance sheet entire continents will functionally be a spec in comparison, and that's really really fucking unlikely to happen.
Doubly so when you consider that there's a question of if they can be profitable to projected needed levels without fundamentally breaking some economics parts, and that this looks a lot like the overbuild of the internet during the dotcom bubble, which was also hideously overvalued at the time, for the time frame they were looking at, despite being THE literal world changing tech.
> "it owes a bunch of people money, so it should be worth more"
"it owes a bunch of people money so the only way for it to not be insolvent is it must be worth X, and if we're talking lower than X this is already a problem"
> For them to pay back what they have on their balance sheet entire continents will functionally be a spec in comparison
Walk me through this math. I’m seeing $900bn Capex over a decade. That’s $90bn required free cash flow a year, on average. If they can do 25% margins, that requires $360bn annual steady-state revenues. That doesn’t require continent-scale anything.
This is the assumption we disagree on. They're net negative by an order of magnitude on most of their current customers. I don't see them flipping to break even + 25%.
Further you're assuming they can turn that on today, when they're still very clearly not ready. Every year the timeline gets worse.
Take them to 2% margin with 8 years to go and you're looking at needing 4.5t annual.
This is normally where I'd say "but 2% feels extreme..." and hedge in the middle, but like...the current pricing isn't even close to 25% margin, it's not even close to break even.
Anthropic has a near-clone in OpenAI (and vice versa). There are multiple smaller competitors, several much cheaper (especially the Chinese models).
Anthropic obviously needs continent-scale balance sheets, I would argue "multiple continent-scale balance sheets", since Google & co make their money by selling everywhere except for China.
Anthropic and OpenAI are never entering China on a massive scale. US relations with Europe are deteriorating and LLMs are <<massive> risks in an adversarial relation so it's not inconceivable Europe at some point starts marginalizing US tech companies, at least LLM labs. I imagine India will want to do something similar at some point.
Where exactly is Anthropic going to make all that steady revenue from, if it can't break out of coding and cybersecurity? Even worse, both those markets are super tech savvy so those cheap rivals are even more dangerous. One slip up the next 5+ years and Anthropic is history.
As a counterpoint I haven't seen opus doing that much better than the latest gpt models? Astra is definitely comparable to fable and sol has been as good as opus for me. So this rings a bit anecdotal and that's probably what people will be seeing affect the growth trajectory too.
IMO Opus 5.5 has quietly been a revelation. It basically requires no hand holding from me given a sufficiently articulated prompt. At this point I'm limited by reviews and end-to-end validation of changes.
Are these two statements not at odds with each other? You can always say a prompt is not sufficiently articulated if you need to add additional handholding and vice versa, thereby invalidating any meaning of measurement.
What I mean is just a modest effort to guide the model up front is adequate for the full end to end session -- one-shotting, basically. I used to go through planning mode prior to 5.5, to make sure it understood my intentions but I no longer need to. I just ramble off the desired outcome and some broad architectural guidelines into wisprflow, hit enter, answer any questions it has and then a PR that tends to only get minor feedback is produced. It's basically fire and forget.
With earlier models simply describing your desired end result often went poorly, and instead you had to break your prompt down into incremental steps. A single “sufficiently articulated“ prompt would result in the model forgetting about some of the things you told it to do by the time it got there.
No, because "sufficiently articulated" is relative to the model. Opus 5.5, often is "please handle this ticket someone assigned me, check this link it might be useful..."
I’ve felt that way since the 4.1 releases. Opus 5.5 is a good model, especially after the disaster at the 4.7 mark.
But sol 6.1 is at least in the conversation as being as good and is much cheaper. I farm out bunches of my tasks to Luna and Haiku these days and have a working prototype of a glm 5.3 workflow at work that shows it being competitive.
The price pressure is very real and if you aren’t on a subscription then there is virtually no reason to pay the Anthropic premium
Not due to capability per se, but writing style and polish is much worse.
It will leave really bad comments in the code, commit messages etc without strong explicit steering. Though I've basically moved away from having the model write any documentation at all.
It also tends to talk very circuitously and referencing things implicitly instead of just being direct and clear.
Certainly it's more capable in debugging, but I much prefer Astra over Sol now.
Of course, Astra has its own problems of code golfing everything. But at least it's very concise and direct
if you go on short form social media, Opus's ability to do agentic work in things like blender, css animation, etc. have spawned hundreds if not thousands of channels of people pumping out content with it. That and all the video game shenanigans (and now Anthropic is even modifying its system prompt to limit some of these things!)
There's definitely unique things that so far only Opus is capable of doing.
Yeah there was a vibe shift towards OpenAI being much more competitive for daily use and then Opus 5.5 hit. Will be curious to look back and see if that shows in data. Of course, could change tomorrow.
Anthropic can remove all access to their LLMs and start replicating entire software companies. Anyone relying on open weight models is at risk of a security breach from even slightly better models. It is ironic when people think Anthropic is the one without moat while they give all their data away.
This is likely true, but doesn’t really tell us much about future revenue growth.
Now they need to compete on price, I think their revenue growth may start to look much more like typical Jevons paradox examples.
I.e as their usage grows exponentially their revenue grows linearly rather than exponentially as it was before.
I’m wondering whether Opus 5.5 would have caused growth in an absolute sense or just growth relative to competitors.
Anthropic’s market share is pretty high and there is not enough total currently addressed market for growth at the expense of a competitor to make a big long-term difference.
Well what I do know as someone who has a bird’s eye view into many enterprises is that many of them are starting to realize they’re bleeding money to Anthropic and are telling their teams to be more efficient with tokens.
I've been throwing opus 5.5 at problems I got stuck on with sonnet in the past and it has breezed through them all. It really reinforced how far the models have come.
> I'm sure there are a lot of people rooting for their IPO to fail but they are still clearly in the lead on coding and will continue to do well until someone definitively passes them.
Then why all the rush to IPO then?
The strongest chinese models from DeepSeek, Kimi and z.ai are already good enough to run for hours on most coding tasks cheaply.
For capital to win they need to get their money out of Anthropic before the bubble deflates. This means dumping the risk on retail. There is only a finite amount of money available to buy AI stock, and the SpaceX IPO sucked a lot up, so it's a fight between Anthropic and OpenAI who is going to get what's left. The loser is left without a chair to stand on when the music stops.
I have zero sense of this myself, am just curious, and not confident anyone really knows, but: I get that its a big deal for software developers when each new model comes out, because its perceived (correctly or not) as the next big boost for their productivity, but what about everyone else who is not a coder? Is there big reactions there to newer models? I don't know a single person who is not a engineer that would be able to tell me if "Astra" or "Opus" are Anthropic things or an OpenAI things respectively, much less when and what the big new version is for any of them. Does this play out for others? Or am I just in my own bubble for some reason?
What does average, non-computer Joe or Jane think when the next big model comes out? "My emails are going to be so much better now, I better stay subscribed"? "I can't wait to see how well it answers my questions"?
One more anecdote: People at our company are very excited for integration of existing industry specific tools and processes.
Some of them do require frontier models but many can do with localish models. Some of these processes (in our industry) are also not really limited by software at all but by the speed heavy machinery can be operated and/or manufactured and/or transported.
I'm certain LLMs are going to continue rapid growth even there but it may be an ideal field for have lesser models work for longer if it saves money in the end.
Of course who knows in the end. I'm certain there is a high chance of this being entirely wrong
> I don't think the "tokens consumed" metric was a very good one.
When mid or high level tech leadership pushes from top-down new things, that look dumb to the engineers, the leadership is either stupid or there are hidden incentives that regular people don't see. In my experience mid level usually matches with the former, and top level with the latter.
Interesting angle to blame the slopmaxers when the AI companies have been pushing maximum usage for a while now.
I assume they’d hoped that the value of the spend would manifest itself and they could raise prices. The opposite happened and companies are instead cutting back.
It sounds to me like they chose a poor marketing and pricing strategy for a product that is very difficult to make cost effective.
Everyone trying to vibecode their next million dollar idea only discover the web is dead. Nobody is looking for anything because that would require leaving the safety of the same handful of sites everyone else is on.
It may have something to do with the summer intern season ending. I know one of the interns hired by my employer used up the allotted AI spend by doing “fun” things, like making Claude write an entire SQL-to-Pandas transpiler in 10,000 lines of Python.
AI put a massive spotlight on all the required but unspoken decision making and coordination that must occur with stakeholders outside their own teams during the development process.
Dysfunctional middle management has always traditionally neglected it or actively undermined it, blaming others when things go wrong and taking credit when things go right.
Naive developers are always more than happy to do what they think is "picking up the slack" not realizing it's a trap.
Perhaps most organizations are just now more wise to the fact that using AI to "accelerate the process" is like arming the monkeys with machine guns?
Lmao a colleague who works elsewhere said some goon at work created an AI leaderboard. He spun up research agents. Top of board achieved. Granted the guys a very solid developer, but he did it to prove how dumb this is.
At the beginning of coding agents really picking up adoption (I guess 12-18 months ago now or so) I remember this sort of thing being so common that some startups were trying to sell a turnkey solution for "leaderboards".
I'm not aware of any of those products really sticking around.
When was the last time anyone used the term "tokenmaxxing" right now?
Exactly, No-one and as predicted it was a fad. [0]
Vibe-coders don't want to risk getting their accounts suspended if you use their models in a certain way.
We are seeing the result of those subsidizations and discounts that Anthropic made this year and how much of this "tokenmaxxing" hype was used to make their revenue numbers look "good".
With the arrival of more cheaper models which are good enough, it doesn't make much sense to use Anthropic's models.
So don't be easily fooled by their revenue projections. Rather question if it is sustainable for them. (Spoiler, it isn't)
Your machinery was not written elegantly. It was sloppily assembled via evolutionary pressures.
A gene gets duplicated, mutates, and suddenly you have new functionality and new fitness criteria.
Try something bad and you die without reproduction.
Our code isn't clean. There's redundancy and extra packing and slop all over the human body.
Embrace it. Stochastically hill climb.
Code is DNA. Evolve it, don't intelligently design it. Fit the curves of your environment quickly and use tests to cull bad behavior and the slopes of your domain.
I think the next cycle of growth will be from increased subscription pricing? Thats what the streaming companies did - start low, keep increasing forever. OpenAI already merging codex / chat and I think most people will have to buy that min subscription or even the higher tier. I am already at the $100/month
I am not saying its a must, but those who are reaping the benefits in someway will end up spending more I guess?
I think most people who care or are tracking it would say we've hit it, and those who disagree just have their goalposts a lot more clearly defined and are quietly waiting.
AI growth is unlikely to be linear. We have seen a lot of hype in the last 12-18 month that give us ideas like "tokenmaxxing". But this is the same cycle that we have been before: hype -> disillusionment -> someone figures out a better way to use it -> next hype phase starts.
As it stands, even the people closest to this technology have barely figured out how to use it effectively.
AI has historically had a habit of disappearing for ten years and coming back once memories fade and the new story sounds enough different from the old one.
I don’t think this era is going to end with us all unemployed, but I do plan to be retired before the next cycle.
Flattened might be an exaggeration but they lost many a customer with boneheaded decisions like blocking subscription access to OpenCode. I don’t think they’re past the point in their growth curve where tinkerers don’t matter anymore. Again, definitely not flattened but not as parabolic as they could be.
> The real question is how many people haven't figured out how to use AI to make their job easier/more productive.
I think the answer, to a reasonable approximation, is “everyone”. Outside software engineering most people aren’t using it for anything useful, and I keep encountering technical people including programmers who don’t use it either, which never fails to surprise me.
One of my projects is a transportation management system (TMS) and of course I have added a lot of features to it including a very capable chat-based UI that can automate a lot of tedious data entry and other tasks for dispatchers and accounting and so on. It gets used but really not very much. I’m starting to realize that I’ve probably taken updates as far as they can go in terms of the ability of people to absorb change. Most people just carry on doing things the way they always have.
We had one person on the admin team who started using Cowork to automate all kinds of stuff. She loved it. Then she left. It hasn’t caught on with anyone else.
This is not to say that things won’t change but I think it will take a while.
my brother uses it to do ocr on his books but, lately, claude has been refusing because of copyright or whatever so i've had to do it for him (using ai, yes, but not anthropic's)
I know firefighters and machinists heavily using it, the only places I see slower adoption is purely because of bad leadership- exclusivity contracts with certain providers or just plain fear.
I think this dynamic is underappreciated. Another thing I see in working with different companies (consulting) and discussing with colleagues at different jobs is that we as humans tend to do a pretty bad job of modeling business outcomes and the dynamics that govern them. There are lots of outcomes where you can identify one contributing role, increase the productivity for that role by a significant factor, and still ultimately not move the needle much, because the constraints span multiple functions.
As an example outside of software engineering, a lot of people in marketing roles have found agents to be incredibly useful for particular tasks. I've talked to multiple people who wouldn't standardly be classified as "technical hires" who are using Claude to manage things like digital advertising. It's valuable here because managing campaigns on a platform like Google or LinkedIn involves a ton of granular tweaking and analysis. Every performance marketer makes tradeoffs around what they're realistically able to spend time doing, and what makes the biggest impact. So if you have 250 campaigns that you manage solo, you likely don't have the time to really manually optimize the bidding and budget for each campaign every couple of days based on new data. You can only try so many new angles, because creating new media is time consuming. You don't have time to construct custom experiences on-page for every particular campaign. There's a whole industry of tools that turn a tidy profit automating parts of this for this reason. And agents are really good at it, because a lot of it is just applying the same analysis over and over again.
But for a lot of startups, increasing traffic from ads 20% while decreasing spend is a good outcome, but it doesn't actually turn into revenue. The product itself might just be limited, and already being discovered by the narrow segment of potential users who find it valuable enough to pay. Or the sales process might be kind of busted, meaning the team will really only close a certain volume of new deals in a given month, no matter how much inbound they have. Or some combination of all of the above.
If it's just one marketer using an agent to optimize their advertising, there's not a lot they can do about the other constraints. And so what you end up with is one employee who feels really productive and sings the praises of Claude, but not a meaningful lift in the higher level outcomes you care about, and the analysis gets messy. In an ideal world, everyone realizes where the other bottlenecks are, but that's a genuinely hard thing to do and people have complicating incentives.
I don't think this is a particularly new problem. It feels identical to any other situation where a department asks for a tool, loves it, and it doesn't really make a difference to the bottom line or make anyone else's life easier in noticeable ways.
We are capped by testing and PR review. The junior devs can spam out a million PRs a day but we keep having production incidents from not carefully scrutinising the changes. So the speed of code generation is limited by how fast we can actually accept those diffs.
Production incidents from not carefully scrutinizing changes is a sign that someone more senior should be putting infrastructure in place to minimize the impact of those incidents.
Feature flags, staggered rollouts, quick rollbacks, smoke testing, etc.
Sure, if you were hand managing every rollout up until now, and relied primarily on unit tests, you’re in for a world of hurt when you give a junior engineer Claude code and let em rip.
But if you have more advanced systems to measure and maintain overall service health, and codify your business logic invariants, you can really start flying these days as long as you’re all aligned on what to build and the potential impact.
I agree, with a big tech deployment process this would be less impactful. But getting from a company used to manual testing and weekly deployments to a fully automated CD pipeline is more work than a single Claude prompt.
That was always the issue... Rewrites were done before LLMs and the results were the same... There was never a point in time where the bottleneck was production of code, it was always review, testing, QA, fixing bugs and edge cases, and that is when only focusing on the technical part of delivery, and leaving out communication and synchronisation within the company and between company and clients...
That strongly depends on context. In larger organisations, this is definitely the case. For smaller startups and teams then the overhead is much smaller and the actual core programming work is much more of a bottleneck.
How so? Or is this just vibes based analysis? I dont really know what you mean by classical engineering, but sure if something can be done on a computer it can infinite monkey theorem its way to a solution. This isnt exactly useful its just an enablement of laziness.
Idk how many people need to hear this, but not everything 'is computa',no matter what Trump says.
Much of the worlds problems are not verifiable with a compiler that can be trained or brute forced against.
Llms are not as useful as ya'll think they are, not everything exists in a web browser, which is why Anthropic et al have saturated their TAM and are now frantically hiring sales people to convince managers in other verticles.
Right, this is what astonishes me about the constant rhetoric/hyperbole about how this is going to impact "all the jobs" and make everyone wildly more productive than ever before. It's a fabulous tool in a limited number of areas, and it's otherwise only handy here and there. But just because it's a fabulous tool for the coding that is so prevalent among HN users does not mean that it's a fabulous tool for everything.
A hammer is fantastic for driving nails, but it's not really all that good at washing a dog.
This is conflating the benefits found within those areas with the ability of AI to actually deliver those things effectively across every single profession. Improving something by 2% vs. revolutionizing an entire industry are wholly different propositions. Maslow's hammer would like a word.
I use classic engineering to refer to the (engineering) disciplines where mother nature messes your day up rather than obscene levels of man made (or machine made) pure complexity ruining your day.
It's easy to understand how LLMs got so good at coding, they can rapidly iterate, self test, and it all happens in their digital universe.
These models now starting to spit grey beard knowledge of precision analog electronics, lightweight fastener design, and chemical surfactant systems is something very very recent. A few years ago I think we were pretty confident that the tech would hit a wall at the digital divide. Well, I can tell you now that it did not. It seems it kinda blasted right past it.
For big ones it’s often network effects and/or friction. Those can be pretty good moats even with LLMs. These models are also nowhere near the point where they can actually reverse engineer complex products.
Network effects have subsequently been; shown to be logarithmic. You’re not getting 100x the benefit from 10x the users. You’re lucky to be getting 12x the benefit and you also have logarithmic increases in costs because there’s no such thing as economies of scale in software. You pay for every order of magnitude in hardware or software development costs.
It was six months last year, it’s on the order of weeks now. Presumably they have unreleased models that are a step ahead still. The moat is capacity and how much of an ecosystem the can build around the llms; eg anthropic figma tool.
i think its a lot deeper than that, compared to deepseek-4.1-flash, glm-5.3-flash and the recent minimax-m3.1-flash [0], the comparison in price (15x cheaper inference cost) and the productivity gains are very similar if not more profound depending your prompt topic (cyber, biochem, etc)
I don't see the issue. If the market is saturated by them, the slowdown seems to indicate to me that it has reached (at least transiently) its intended audience. If the end goal is that every person on earth has a claude subscription (or whatever your choice is) then yeah that is worrying, but I don't deem that realistic.
Why do you say this? If they get to even 100B RRR they will pay off all of their commitments in <10 years with money to spare. Does that seem so impossible to you?
my simplified take is the headwind is on two ends - smaller models being sufficient enough & open weight models then second utility of 'a.i' is maximal when deployed with humans i.e engineers applying a.i on top of current applications putting an agent layer on top.
the second part is not sexy - but we see all a.i labs having fde arms e.g ode, microsoft frontier company etc but then again that's the part you can't hype.
The conclusion that this is a bubble seems arbitrary and disconnected from the several true facts outlined in the article. Anthropic's growth _is_ slowing, albeit still strong at ~47%. They do have enormous compute obligations, and it will require a lot of growth to meet them. However, they also increased revenue per dollar spent on compute from $0.40 to $2.08 in five quarters and hold $131.7bn in cash.
Keep in mind this growth has mostly been sustained by low-level business automations, coding, and chatbots. With the adoption of products like personal agents, e.g., Meta's Muse, I am pretty optimistic there will be demand and growth around this level will be sustained for a few quarters. Doubtful we will ever see another ~150% growth quarter.
> revenue per dollar spent on compute from $0.40 to $2.08
So that’s ignoring labor and perhaps facilities? That’s like saying we’ve hit net positive fusion by only considering the power produced by the laser and ignoring the 80% power losses in the feed-in.
Zitron's literally just some normie that got pushed by the algo a bit harder than other shocked faces with flames and red arrows and no industry insight.
He says a lot of things, but one specific thing I remember from weeks ago is that he said Anthropic growth slowed on Q3, and that's why they decided to push for the IPO (and use only Q2 numbers).
Right, but a comparison is always one more implicit derivative. If you look at revenue itself, it's growing or shrinking, that's first derivative. So there's one more derivative here than usual?
I guess the interesting thing would be to look at actual company assets/value rather than revenue; revenue already in itself is a derivative. I.e., contrast their revenue against expenses. Last I heard those numbers are deep red, aren't they?
Growth is first derivative. Deceleration is second derivative. Focusing on second not first I think was the point, not implying there is some nth derivative under discussion.
Not at all, the subscriptions have an adjusted gross margin of approximately 85%. Very profitable! The training is a different story though. How long will the subs last if they stop training? Also, why on earth would they put on the breaks and try to be profitable when they have plenty of funds and are in the growth stage?
The subscriptions are a key part of the engine to ensure The Machine can keep improving itself by harvesting all that data and feeding it back in. Many of the enterprise contracts forbid the lab from training on their data, so the subscriptions are a form of gold.
Their financial statements suggest the answer, even when you take into account the cost of training the model, is now 'not at all'. They are profitable, the main questions for their valuation are 1) can they keep their margins, 2) how big can they grow?, and 3) can this growth match what they have committed to?
Question one is how do their investors get paid back. I found Series E through Series H totaling $111.5 billion in investments. Since series E was $3.5 I’m assuming all the previous rounds added up to less than that, so let’s say 115 billion at the outside.
That debt will transfer to public shareholders at the IPO but also will double or triple in the process, as the investors expect to be paid very, very well. So the current shareholders are looking to extract something over $400 billion from the IPO.
With the current revenue they might pull off that P/E. But they’ll still be extracting some of that value from the customer base, because shareholders will demand it.
Ah the beauty of circular financing. By owning the company who pays me to provide a service, I stand to gain massively by giving them a discount, making them look profitable, and fooling investors in to providing my exit liquidity.
If you ever wonder why Anthropic is going all out to try and fearmonger their way to an open weights ban, this graph will provide all the answer you need.
- Would you like to get paid a million dollars right now, or get paid a penny today that doubles everyday for 30 days.
- hmmm, why 30 days? Make it 60 days and I'll take the penny.
The effort needed to double everyday, or month is realistic at first. But then you can't just keep doubling without swallowing the universe along the way.
You could in theory be responsible for the destruction of the entire solar system in order to pay you. Sit on your throne atop a mountain of skulls and weep.
I think one thing that could explain it is that some of the big tech companies might have been big customers of theirs, and now they have alternatives that, according to recent news, their employees are being pushed to use instead. Some of those reports mentioned internal usage getting cut in half. If this is true, it doesn't necessarily mean their growth has stalled.
Employees were and are pushing for more model and harness diversity too I promise.
But perhaps for different reasons than Microsoft, Meta, and maybe Adobe leadership are now. See, if they are all in on Claude how do they sue Anthropic for assisting in copyright violations and IP theft? The services would be cut immediately.
Well when they don't need them anymore..
This is why Anthropic is heaping on more and more model restrictions running up to IPO. I believe it's a smokescreen for the IP risk they outlined in their IPO filing..
1) customer concentration risk
2) when those customers use a diverse set of models - so too will the rest - they’re not idiots who want to pay more than they should
Aka % growth rate decreasing sharply and eventually flattening out. Within 2-3 quarters.
I've been saying it for a while now but if someone sold me a box with model perf from ... I guess November 2025 then that's decent enough for my usage. Tool interaction is the magic, I don't need much if any more innate intelligence. Things are fast enough.
I wonder if Anthropic will "see the light" and stop trying to burn a bunch of money and sell a thing they already have (which is decently useful). Very ready to be proven wrong in any direction
wow really? Nov 2025 I was still fighting blatant hallucinations in helm charts. Now I'm two prompts away from a new build published on the app store and 5 video reels in the queue complete with captions and everything
The thing is I'm not spinning up a new app every day, I'm just working on a large existing system with enough constraints. From that perspective tools + the general rigor I already try to apply means that I can get some value out of LLMs without waiting for it to do something "good".
"I can one-shot a new thing" is interesting but definitionally a thing is new only once. Maybe if I worked in a space where we had way more throwaway things to work on.
At the end of the day I want to understand the output anyways, so it one shotting a thing vs me going back and forth with it in smaller increments is a mixed bag anyways.
I wouldn't really be surprised if better model efficiency was part of the reason for a revenue stall, but I would also expect their costs to have dropped a fair bit too (although I have no idea whether that corresponds to an actual cost reduction or if it's all tied up in hardware and land that they already bought and will need to hit more utilisation of now.)
I think the first implying the second might be a bit of a stretch. At some point, companies are going to start looking at their AI spending and start negotiating deals. OpenAI is right behind them and Anthropic is around 8x more expensive (Sol 6.1 vs Opus 5.5 based on Artificial Intelligence's cost per task; cost per token is a somewhat useless metric when different models use vastly different numbers of tokens for the same thing). Chinese models like Deepseek 4.1 Flash are cheap, can be self-hosted, and while certainly behind Claude Opus 5.5, are still good.
Lots of companies buy "inferior" products like Microsoft Teams. Over the next 5 years, it seems likely that many models will get really good to the point that people often won't be able to tell any difference. Even if something isn't "definitively" better, companies might say "our engineers can't tell the difference and we're cutting our costs by 85%."
It's not absurd to think that a company would use its own harness or an open source harness and/or put a proxy in to route requests variably. For two months, they have engineers using different models unbeknownst to them and they measure things like how often the engineers have to correct or redirect the model. At the end of two months, they might find little difference in performance and switch to a much lower cost model.
It's not like it'd be crazy hard to analyze workers frustration as they did their jobs and figure out if one model was actually better. They could log all the conversations and it's not like AI can't easily determine things like "no, that's completely wrong" as a phrase showing that the agent was really off base.
I think a big part of the next 5-10 years is going to be about cost/efficiency.
I want AI and these companies to succeed. And I especially want new companies to succeed. But I want them to be valued far more realistically. Because if they have to be worth trillions, then there’s a real motivation to do what it takes to realize that valuation. And that’s how you birth a Zuckerberg.
I’m not rooting for them to fail. I’m rooting for the correction to happen sooner than later.
They are. That's the problem. The obligations that these companies are, on paper, supposed to meet require these valuations.
If you value Anthropic at $500b that's still a fuckload and vastly less than their obligations and a lot of people are going to say "well wait a minute how am I going to get paid".
The amount of spend going on, and planned, is insane and underpins a lot of this, and is why people are skeptical of the valuations. It has nothing to do with "Wanting" AI to fail and just a reasonable bet based on the math. For them to pay back what they have on their balance sheet entire continents will functionally be a spec in comparison, and that's really really fucking unlikely to happen.
Doubly so when you consider that there's a question of if they can be profitable to projected needed levels without fundamentally breaking some economics parts, and that this looks a lot like the overbuild of the internet during the dotcom bubble, which was also hideously overvalued at the time, for the time frame they were looking at, despite being THE literal world changing tech.
How does that make sense lol
"it owes a bunch of people money, so it should be worth more"
i don't...
"it owes a bunch of people money so the only way for it to not be insolvent is it must be worth X, and if we're talking lower than X this is already a problem"
Walk me through this math. I’m seeing $900bn Capex over a decade. That’s $90bn required free cash flow a year, on average. If they can do 25% margins, that requires $360bn annual steady-state revenues. That doesn’t require continent-scale anything.
This is the assumption we disagree on. They're net negative by an order of magnitude on most of their current customers. I don't see them flipping to break even + 25%.
Further you're assuming they can turn that on today, when they're still very clearly not ready. Every year the timeline gets worse.
Take them to 2% margin with 8 years to go and you're looking at needing 4.5t annual.
This is normally where I'd say "but 2% feels extreme..." and hedge in the middle, but like...the current pricing isn't even close to 25% margin, it's not even close to break even.
Wha???
There are 13 companies in <<total>>, on the <<entire planet>>, that make more than that: https://companiesmarketcap.com/largest-companies-by-revenue/
Most of those needed decades to reach this point.
Anthropic has a near-clone in OpenAI (and vice versa). There are multiple smaller competitors, several much cheaper (especially the Chinese models).
Anthropic obviously needs continent-scale balance sheets, I would argue "multiple continent-scale balance sheets", since Google & co make their money by selling everywhere except for China.
Anthropic and OpenAI are never entering China on a massive scale. US relations with Europe are deteriorating and LLMs are <<massive> risks in an adversarial relation so it's not inconceivable Europe at some point starts marginalizing US tech companies, at least LLM labs. I imagine India will want to do something similar at some point.
Where exactly is Anthropic going to make all that steady revenue from, if it can't break out of coding and cybersecurity? Even worse, both those markets are super tech savvy so those cheap rivals are even more dangerous. One slip up the next 5+ years and Anthropic is history.
> given a sufficiently articulated prompt
Are these two statements not at odds with each other? You can always say a prompt is not sufficiently articulated if you need to add additional handholding and vice versa, thereby invalidating any meaning of measurement.
But sol 6.1 is at least in the conversation as being as good and is much cheaper. I farm out bunches of my tasks to Luna and Haiku these days and have a working prototype of a glm 5.3 workflow at work that shows it being competitive.
The price pressure is very real and if you aren’t on a subscription then there is virtually no reason to pay the Anthropic premium
Not due to capability per se, but writing style and polish is much worse.
It will leave really bad comments in the code, commit messages etc without strong explicit steering. Though I've basically moved away from having the model write any documentation at all.
It also tends to talk very circuitously and referencing things implicitly instead of just being direct and clear.
Certainly it's more capable in debugging, but I much prefer Astra over Sol now.
Of course, Astra has its own problems of code golfing everything. But at least it's very concise and direct
There's definitely unique things that so far only Opus is capable of doing.
"I have a girlfriend for real she just goes to a different school"
I ping-pong between different agents all day long and don't necessarily agree that Anthropic is clearly in the lead on coding at this point.
Anthropic’s market share is pretty high and there is not enough total currently addressed market for growth at the expense of a competitor to make a big long-term difference.
Then why all the rush to IPO then?
The strongest chinese models from DeepSeek, Kimi and z.ai are already good enough to run for hours on most coding tasks cheaply.
What does average, non-computer Joe or Jane think when the next big model comes out? "My emails are going to be so much better now, I better stay subscribed"? "I can't wait to see how well it answers my questions"?
Some of them do require frontier models but many can do with localish models. Some of these processes (in our industry) are also not really limited by software at all but by the speed heavy machinery can be operated and/or manufactured and/or transported.
I'm certain LLMs are going to continue rapid growth even there but it may be an ideal field for have lesser models work for longer if it saves money in the end.
Of course who knows in the end. I'm certain there is a high chance of this being entirely wrong
Actually I see very varying results in my work.
Sometimes I can let an AI refactor an entire codebase and then (in the same codebase) it generates absolute shitcode.
When mid or high level tech leadership pushes from top-down new things, that look dumb to the engineers, the leadership is either stupid or there are hidden incentives that regular people don't see. In my experience mid level usually matches with the former, and top level with the latter.
I assume they’d hoped that the value of the spend would manifest itself and they could raise prices. The opposite happened and companies are instead cutting back.
It sounds to me like they chose a poor marketing and pricing strategy for a product that is very difficult to make cost effective.
Dysfunctional middle management has always traditionally neglected it or actively undermined it, blaming others when things go wrong and taking credit when things go right.
Naive developers are always more than happy to do what they think is "picking up the slack" not realizing it's a trap.
Perhaps most organizations are just now more wise to the fact that using AI to "accelerate the process" is like arming the monkeys with machine guns?
I'm not aware of any of those products really sticking around.
We are seeing the result of those subsidizations and discounts that Anthropic made this year and how much of this "tokenmaxxing" hype was used to make their revenue numbers look "good".
With the arrival of more cheaper models which are good enough, it doesn't make much sense to use Anthropic's models.
So don't be easily fooled by their revenue projections. Rather question if it is sustainable for them. (Spoiler, it isn't)
[0] https://news.ycombinator.com/item?id=49330214
Your machinery was not written elegantly. It was sloppily assembled via evolutionary pressures.
A gene gets duplicated, mutates, and suddenly you have new functionality and new fitness criteria.
Try something bad and you die without reproduction.
Our code isn't clean. There's redundancy and extra packing and slop all over the human body.
Embrace it. Stochastically hill climb.
Code is DNA. Evolve it, don't intelligently design it. Fit the curves of your environment quickly and use tests to cull bad behavior and the slopes of your domain.
Anthropic released their Q3 numbers today which show:
- $16.96B in revenue Q3
- Up 47% quarter over quarter (Q2 was $11.6B)
- Up >1000% year over year
I wouldn't say their growth "flattened"? Their price drops in September dramatically increased Claude token consumption per OpenRouter
Sources: https://www.anthropic.com/news/s1-sec; https://openrouter.ai/anthropic
https://isaiprofitable.com/
I am not saying its a must, but those who are reaping the benefits in someway will end up spending more I guess?
As it stands, even the people closest to this technology have barely figured out how to use it effectively.
Not saying that applies to AI, but it certainly applies sometimes.
I don’t think this era is going to end with us all unemployed, but I do plan to be retired before the next cycle.
Most likely the combined revenue growth continued just as fast, but OpenAI took a lot of market share with a model that's ~as good.
If true, then the current market sentiment is too pessimistic because demand for AI (and chips) has continued growing.
Especially with this latest crop of models, they seem to be pretty damn competent in classic engineering rather than just software engineering.
I think the answer, to a reasonable approximation, is “everyone”. Outside software engineering most people aren’t using it for anything useful, and I keep encountering technical people including programmers who don’t use it either, which never fails to surprise me.
One of my projects is a transportation management system (TMS) and of course I have added a lot of features to it including a very capable chat-based UI that can automate a lot of tedious data entry and other tasks for dispatchers and accounting and so on. It gets used but really not very much. I’m starting to realize that I’ve probably taken updates as far as they can go in terms of the ability of people to absorb change. Most people just carry on doing things the way they always have.
We had one person on the admin team who started using Cowork to automate all kinds of stuff. She loved it. Then she left. It hasn’t caught on with anyone else.
This is not to say that things won’t change but I think it will take a while.
As an example outside of software engineering, a lot of people in marketing roles have found agents to be incredibly useful for particular tasks. I've talked to multiple people who wouldn't standardly be classified as "technical hires" who are using Claude to manage things like digital advertising. It's valuable here because managing campaigns on a platform like Google or LinkedIn involves a ton of granular tweaking and analysis. Every performance marketer makes tradeoffs around what they're realistically able to spend time doing, and what makes the biggest impact. So if you have 250 campaigns that you manage solo, you likely don't have the time to really manually optimize the bidding and budget for each campaign every couple of days based on new data. You can only try so many new angles, because creating new media is time consuming. You don't have time to construct custom experiences on-page for every particular campaign. There's a whole industry of tools that turn a tidy profit automating parts of this for this reason. And agents are really good at it, because a lot of it is just applying the same analysis over and over again.
But for a lot of startups, increasing traffic from ads 20% while decreasing spend is a good outcome, but it doesn't actually turn into revenue. The product itself might just be limited, and already being discovered by the narrow segment of potential users who find it valuable enough to pay. Or the sales process might be kind of busted, meaning the team will really only close a certain volume of new deals in a given month, no matter how much inbound they have. Or some combination of all of the above.
If it's just one marketer using an agent to optimize their advertising, there's not a lot they can do about the other constraints. And so what you end up with is one employee who feels really productive and sings the praises of Claude, but not a meaningful lift in the higher level outcomes you care about, and the analysis gets messy. In an ideal world, everyone realizes where the other bottlenecks are, but that's a genuinely hard thing to do and people have complicating incentives.
I don't think this is a particularly new problem. It feels identical to any other situation where a department asks for a tool, loves it, and it doesn't really make a difference to the bottom line or make anyone else's life easier in noticeable ways.
Feature flags, staggered rollouts, quick rollbacks, smoke testing, etc.
Sure, if you were hand managing every rollout up until now, and relied primarily on unit tests, you’re in for a world of hurt when you give a junior engineer Claude code and let em rip.
But if you have more advanced systems to measure and maintain overall service health, and codify your business logic invariants, you can really start flying these days as long as you’re all aligned on what to build and the potential impact.
Idk how many people need to hear this, but not everything 'is computa',no matter what Trump says.
Much of the worlds problems are not verifiable with a compiler that can be trained or brute forced against.
Llms are not as useful as ya'll think they are, not everything exists in a web browser, which is why Anthropic et al have saturated their TAM and are now frantically hiring sales people to convince managers in other verticles.
Turns out most people dont need or want AI.
A hammer is fantastic for driving nails, but it's not really all that good at washing a dog.
It's easy to understand how LLMs got so good at coding, they can rapidly iterate, self test, and it all happens in their digital universe.
These models now starting to spit grey beard knowledge of precision analog electronics, lightweight fastener design, and chemical surfactant systems is something very very recent. A few years ago I think we were pretty confident that the tech would hit a wall at the digital divide. Well, I can tell you now that it did not. It seems it kinda blasted right past it.
The idea is: if you have a substantive point, make it thoughtfully; if not, please don't comment until you do.
https://news.ycombinator.com/newsguidelines.html
You can go very far in life with a thing that isn't futzy
AI is just a compute commodity with a value add.
So that leaves...
[0] https://x.com/jumperz/status/2112226308868436154
the second part is not sexy - but we see all a.i labs having fde arms e.g ode, microsoft frontier company etc but then again that's the part you can't hype.
Keep in mind this growth has mostly been sustained by low-level business automations, coding, and chatbots. With the adoption of products like personal agents, e.g., Meta's Muse, I am pretty optimistic there will be demand and growth around this level will be sustained for a few quarters. Doubtful we will ever see another ~150% growth quarter.
So that’s ignoring labor and perhaps facilities? That’s like saying we’ve hit net positive fusion by only considering the power produced by the laser and ignoring the 80% power losses in the feed-in.
EDIT: okay, not months, weeks. Feels like forever.
I guess the interesting thing would be to look at actual company assets/value rather than revenue; revenue already in itself is a derivative. I.e., contrast their revenue against expenses. Last I heard those numbers are deep red, aren't they?
The company was founded under the principle they will violate laws to acquire training material
That debt will transfer to public shareholders at the IPO but also will double or triple in the process, as the investors expect to be paid very, very well. So the current shareholders are looking to extract something over $400 billion from the IPO.
With the current revenue they might pull off that P/E. But they’ll still be extracting some of that value from the customer base, because shareholders will demand it.
Subscriptions are a growing minority of revenue.
- hmmm, why 30 days? Make it 60 days and I'll take the penny.
The effort needed to double everyday, or month is realistic at first. But then you can't just keep doubling without swallowing the universe along the way.
~5 quadrillion after 60
Discussed here a few weeks ago: https://news.ycombinator.com/item?id=49997161
But perhaps for different reasons than Microsoft, Meta, and maybe Adobe leadership are now. See, if they are all in on Claude how do they sue Anthropic for assisting in copyright violations and IP theft? The services would be cut immediately.
Well when they don't need them anymore..
This is why Anthropic is heaping on more and more model restrictions running up to IPO. I believe it's a smokescreen for the IP risk they outlined in their IPO filing..
1) customer concentration risk 2) when those customers use a diverse set of models - so too will the rest - they’re not idiots who want to pay more than they should
Aka % growth rate decreasing sharply and eventually flattening out. Within 2-3 quarters.
Understood?
I wonder if Anthropic will "see the light" and stop trying to burn a bunch of money and sell a thing they already have (which is decently useful). Very ready to be proven wrong in any direction
"I can one-shot a new thing" is interesting but definitionally a thing is new only once. Maybe if I worked in a space where we had way more throwaway things to work on.
At the end of the day I want to understand the output anyways, so it one shotting a thing vs me going back and forth with it in smaller increments is a mixed bag anyways.